Overview

Dataset statistics

Number of variables81
Number of observations1460
Missing cells6965
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory924.0 KiB
Average record size in memory648.1 B

Variable types

Numeric30
Categorical50
Boolean1

Alerts

LotFrontage is highly correlated with LotAreaHigh correlation
LotArea is highly correlated with LotFrontageHigh correlation
OverallQual is highly correlated with YearBuilt and 7 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 6 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtUnfSF and 1 other fieldsHigh correlation
BsmtUnfSF is highly correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 6 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
Fireplaces is highly correlated with SalePriceHigh correlation
GarageYrBlt is highly correlated with OverallQual and 6 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 6 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 11 other fieldsHigh correlation
OverallQual is highly correlated with YearBuilt and 8 other fieldsHigh correlation
YearBuilt is highly correlated with OverallQual and 4 other fieldsHigh correlation
YearRemodAdd is highly correlated with OverallQual and 3 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with TotalBsmtSF and 1 other fieldsHigh correlation
TotalBsmtSF is highly correlated with OverallQual and 3 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 2 other fieldsHigh correlation
2ndFlrSF is highly correlated with GrLivArea and 3 other fieldsHigh correlation
GrLivArea is highly correlated with OverallQual and 6 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1High correlation
FullBath is highly correlated with OverallQual and 3 other fieldsHigh correlation
HalfBath is highly correlated with 2ndFlrSFHigh correlation
BedroomAbvGr is highly correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with 2ndFlrSF and 4 other fieldsHigh correlation
GarageYrBlt is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageCars is highly correlated with OverallQual and 4 other fieldsHigh correlation
GarageArea is highly correlated with OverallQual and 3 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 9 other fieldsHigh correlation
Id is highly correlated with BsmtFinSF2 and 10 other fieldsHigh correlation
MSSubClass is highly correlated with BsmtFinSF2 and 9 other fieldsHigh correlation
LotFrontage is highly correlated with LotArea and 9 other fieldsHigh correlation
LotArea is highly correlated with LotFrontage and 9 other fieldsHigh correlation
OverallQual is highly correlated with BsmtFinSF2 and 9 other fieldsHigh correlation
OverallCond is highly correlated with BsmtFinSF2 and 12 other fieldsHigh correlation
YearBuilt is highly correlated with YearRemodAdd and 10 other fieldsHigh correlation
YearRemodAdd is highly correlated with YearBuilt and 10 other fieldsHigh correlation
MasVnrArea is highly correlated with BsmtFinSF2 and 8 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with BsmtFinSF2 and 9 other fieldsHigh correlation
BsmtFinSF2 is highly correlated with Id and 19 other fieldsHigh correlation
BsmtUnfSF is highly correlated with LowQualFinSF and 9 other fieldsHigh correlation
TotalBsmtSF is highly correlated with 1stFlrSF and 9 other fieldsHigh correlation
1stFlrSF is highly correlated with TotalBsmtSF and 9 other fieldsHigh correlation
2ndFlrSF is highly correlated with LowQualFinSF and 8 other fieldsHigh correlation
LowQualFinSF is highly correlated with Id and 24 other fieldsHigh correlation
GrLivArea is highly correlated with BsmtFullBath and 8 other fieldsHigh correlation
BsmtFullBath is highly correlated with Id and 15 other fieldsHigh correlation
BsmtHalfBath is highly correlated with Id and 25 other fieldsHigh correlation
FullBath is highly correlated with OverallCond and 8 other fieldsHigh correlation
HalfBath is highly correlated with Id and 15 other fieldsHigh correlation
BedroomAbvGr is highly correlated with LowQualFinSF and 6 other fieldsHigh correlation
KitchenAbvGr is highly correlated with Id and 26 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with EnclosedPorch and 4 other fieldsHigh correlation
Fireplaces is highly correlated with BsmtHalfBath and 5 other fieldsHigh correlation
GarageYrBlt is highly correlated with YearBuilt and 6 other fieldsHigh correlation
GarageCars is highly correlated with OverallCond and 6 other fieldsHigh correlation
GarageArea is highly correlated with GarageCars and 5 other fieldsHigh correlation
WoodDeckSF is highly correlated with EnclosedPorch and 4 other fieldsHigh correlation
OpenPorchSF is highly correlated with EnclosedPorch and 4 other fieldsHigh correlation
EnclosedPorch is highly correlated with Id and 33 other fieldsHigh correlation
3SsnPorch is highly correlated with Id and 33 other fieldsHigh correlation
ScreenPorch is highly correlated with Id and 33 other fieldsHigh correlation
PoolArea is highly correlated with Id and 33 other fieldsHigh correlation
MiscVal is highly correlated with Id and 33 other fieldsHigh correlation
SalePrice is highly correlated with OverallQual and 1 other fieldsHigh correlation
MSSubClass is highly correlated with Alley and 13 other fieldsHigh correlation
MSZoning is highly correlated with Alley and 2 other fieldsHigh correlation
LotFrontage is highly correlated with Neighborhood and 6 other fieldsHigh correlation
LotArea is highly correlated with LandSlopeHigh correlation
Alley is highly correlated with MSSubClass and 14 other fieldsHigh correlation
LandContour is highly correlated with NeighborhoodHigh correlation
LotConfig is highly correlated with PoolQCHigh correlation
LandSlope is highly correlated with LotArea and 2 other fieldsHigh correlation
Neighborhood is highly correlated with MSSubClass and 35 other fieldsHigh correlation
Condition2 is highly correlated with RoofStyle and 5 other fieldsHigh correlation
BldgType is highly correlated with MSSubClass and 3 other fieldsHigh correlation
HouseStyle is highly correlated with MSSubClass and 7 other fieldsHigh correlation
OverallQual is highly correlated with Alley and 18 other fieldsHigh correlation
OverallCond is highly correlated with Alley and 4 other fieldsHigh correlation
YearBuilt is highly correlated with MSSubClass and 28 other fieldsHigh correlation
YearRemodAdd is highly correlated with Neighborhood and 12 other fieldsHigh correlation
RoofStyle is highly correlated with LandSlope and 3 other fieldsHigh correlation
RoofMatl is highly correlated with LotFrontage and 7 other fieldsHigh correlation
Exterior1st is highly correlated with Alley and 11 other fieldsHigh correlation
Exterior2nd is highly correlated with MSSubClass and 12 other fieldsHigh correlation
MasVnrType is highly correlated with Neighborhood and 5 other fieldsHigh correlation
MasVnrArea is highly correlated with OverallQual and 4 other fieldsHigh correlation
ExterQual is highly correlated with MSSubClass and 16 other fieldsHigh correlation
ExterCond is highly correlated with OverallCond and 1 other fieldsHigh correlation
Foundation is highly correlated with Alley and 11 other fieldsHigh correlation
BsmtQual is highly correlated with MSSubClass and 15 other fieldsHigh correlation
BsmtCond is highly correlated with OverallQual and 1 other fieldsHigh correlation
BsmtExposure is highly correlated with PoolQCHigh correlation
BsmtFinType1 is highly correlated with Neighborhood and 6 other fieldsHigh correlation
BsmtFinSF1 is highly correlated with LotFrontage and 11 other fieldsHigh correlation
BsmtFinType2 is highly correlated with BsmtFinType1 and 2 other fieldsHigh correlation
BsmtFinSF2 is highly correlated with BsmtFinType2 and 2 other fieldsHigh correlation
BsmtUnfSF is highly correlated with BsmtFinType1 and 3 other fieldsHigh correlation
TotalBsmtSF is highly correlated with LotFrontage and 9 other fieldsHigh correlation
Heating is highly correlated with Foundation and 2 other fieldsHigh correlation
HeatingQC is highly correlated with Neighborhood and 6 other fieldsHigh correlation
CentralAir is highly correlated with YearBuilt and 4 other fieldsHigh correlation
Electrical is highly correlated with GarageYrBlt and 2 other fieldsHigh correlation
1stFlrSF is highly correlated with MSSubClass and 17 other fieldsHigh correlation
2ndFlrSF is highly correlated with MSSubClass and 11 other fieldsHigh correlation
LowQualFinSF is highly correlated with Heating and 1 other fieldsHigh correlation
GrLivArea is highly correlated with LotFrontage and 20 other fieldsHigh correlation
BsmtFullBath is highly correlated with BsmtFinSF1 and 2 other fieldsHigh correlation
BsmtHalfBath is highly correlated with SaleConditionHigh correlation
FullBath is highly correlated with MSSubClass and 16 other fieldsHigh correlation
HalfBath is highly correlated with MSSubClass and 3 other fieldsHigh correlation
BedroomAbvGr is highly correlated with MSSubClass and 6 other fieldsHigh correlation
KitchenAbvGr is highly correlated with MSSubClass and 2 other fieldsHigh correlation
KitchenQual is highly correlated with Alley and 14 other fieldsHigh correlation
TotRmsAbvGrd is highly correlated with HouseStyle and 8 other fieldsHigh correlation
Fireplaces is highly correlated with Neighborhood and 3 other fieldsHigh correlation
FireplaceQu is highly correlated with Neighborhood and 4 other fieldsHigh correlation
GarageType is highly correlated with Neighborhood and 2 other fieldsHigh correlation
GarageYrBlt is highly correlated with Alley and 20 other fieldsHigh correlation
GarageFinish is highly correlated with Neighborhood and 11 other fieldsHigh correlation
GarageCars is highly correlated with Neighborhood and 9 other fieldsHigh correlation
GarageArea is highly correlated with Neighborhood and 13 other fieldsHigh correlation
GarageQual is highly correlated with YearBuilt and 3 other fieldsHigh correlation
GarageCond is highly correlated with Electrical and 2 other fieldsHigh correlation
PavedDrive is highly correlated with Neighborhood and 1 other fieldsHigh correlation
WoodDeckSF is highly correlated with PoolQCHigh correlation
OpenPorchSF is highly correlated with Condition2 and 2 other fieldsHigh correlation
EnclosedPorch is highly correlated with PoolAreaHigh correlation
PoolArea is highly correlated with RoofMatl and 5 other fieldsHigh correlation
PoolQC is highly correlated with LotFrontage and 22 other fieldsHigh correlation
MiscFeature is highly correlated with RoofStyle and 10 other fieldsHigh correlation
MiscVal is highly correlated with Condition2 and 1 other fieldsHigh correlation
YrSold is highly correlated with PoolQCHigh correlation
SaleType is highly correlated with SaleConditionHigh correlation
SaleCondition is highly correlated with BsmtHalfBath and 2 other fieldsHigh correlation
SalePrice is highly correlated with Alley and 21 other fieldsHigh correlation
LotFrontage has 259 (17.7%) missing values Missing
Alley has 1369 (93.8%) missing values Missing
BsmtQual has 37 (2.5%) missing values Missing
BsmtCond has 37 (2.5%) missing values Missing
BsmtExposure has 38 (2.6%) missing values Missing
BsmtFinType1 has 37 (2.5%) missing values Missing
BsmtFinType2 has 38 (2.6%) missing values Missing
FireplaceQu has 690 (47.3%) missing values Missing
GarageType has 81 (5.5%) missing values Missing
GarageYrBlt has 81 (5.5%) missing values Missing
GarageFinish has 81 (5.5%) missing values Missing
GarageQual has 81 (5.5%) missing values Missing
GarageCond has 81 (5.5%) missing values Missing
PoolQC has 1453 (99.5%) missing values Missing
Fence has 1179 (80.8%) missing values Missing
MiscFeature has 1406 (96.3%) missing values Missing
MiscVal is highly skewed (γ1 = 24.47679419) Skewed
Id is uniformly distributed Uniform
Id has unique values Unique
MasVnrArea has 861 (59.0%) zeros Zeros
BsmtFinSF1 has 467 (32.0%) zeros Zeros
BsmtFinSF2 has 1293 (88.6%) zeros Zeros
BsmtUnfSF has 118 (8.1%) zeros Zeros
TotalBsmtSF has 37 (2.5%) zeros Zeros
2ndFlrSF has 829 (56.8%) zeros Zeros
LowQualFinSF has 1434 (98.2%) zeros Zeros
GarageArea has 81 (5.5%) zeros Zeros
WoodDeckSF has 761 (52.1%) zeros Zeros
OpenPorchSF has 656 (44.9%) zeros Zeros
EnclosedPorch has 1252 (85.8%) zeros Zeros
3SsnPorch has 1436 (98.4%) zeros Zeros
ScreenPorch has 1344 (92.1%) zeros Zeros
PoolArea has 1453 (99.5%) zeros Zeros
MiscVal has 1408 (96.4%) zeros Zeros

Reproduction

Analysis started2022-06-08 01:24:36.644013
Analysis finished2022-06-08 01:26:20.420903
Duration1 minute and 43.78 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean730.5
Minimum1
Maximum1460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:20.494887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.95
Q1365.75
median730.5
Q31095.25
95-th percentile1387.05
Maximum1460
Range1459
Interquartile range (IQR)729.5

Descriptive statistics

Standard deviation421.6100094
Coefficient of variation (CV)0.577152648
Kurtosis-1.2
Mean730.5
Median Absolute Deviation (MAD)365
Skewness0
Sum1066530
Variance177755
MonotonicityStrictly increasing
2022-06-07T22:26:20.611935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
0.1%
9821
 
0.1%
9801
 
0.1%
9791
 
0.1%
9781
 
0.1%
9771
 
0.1%
9761
 
0.1%
9751
 
0.1%
9741
 
0.1%
9731
 
0.1%
Other values (1450)1450
99.3%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
101
0.1%
ValueCountFrequency (%)
14601
0.1%
14591
0.1%
14581
0.1%
14571
0.1%
14561
0.1%
14551
0.1%
14541
0.1%
14531
0.1%
14521
0.1%
14511
0.1%

MSSubClass
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726027
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:20.713935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.30057099
Coefficient of variation (CV)0.7434553226
Kurtosis1.580187965
Mean56.89726027
Median Absolute Deviation (MAD)30
Skewness1.407656747
Sum83070
Variance1789.338306
MonotonicityNot monotonic
2022-06-07T22:26:20.792887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20536
36.7%
60299
20.5%
50144
 
9.9%
12087
 
6.0%
3069
 
4.7%
16063
 
4.3%
7060
 
4.1%
8058
 
4.0%
9052
 
3.6%
19030
 
2.1%
Other values (5)62
 
4.2%
ValueCountFrequency (%)
20536
36.7%
3069
 
4.7%
404
 
0.3%
4512
 
0.8%
50144
 
9.9%
60299
20.5%
7060
 
4.1%
7516
 
1.1%
8058
 
4.0%
8520
 
1.4%
ValueCountFrequency (%)
19030
 
2.1%
18010
 
0.7%
16063
 
4.3%
12087
 
6.0%
9052
 
3.6%
8520
 
1.4%
8058
 
4.0%
7516
 
1.1%
7060
 
4.1%
60299
20.5%

MSZoning
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.034246575
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL1151
78.8%
RM218
 
14.9%
FV65
 
4.5%
RH16
 
1.1%
C (all)10
 
0.7%

Length

2022-06-07T22:26:20.884248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:20.941245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rl1151
78.3%
rm218
 
14.8%
fv65
 
4.4%
rh16
 
1.1%
c10
 
0.7%
all10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LotFrontage
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct110
Distinct (%)9.2%
Missing259
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean70.04995837
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:21.018520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile34
Q159
median69
Q380
95-th percentile107
Maximum313
Range292
Interquartile range (IQR)21

Descriptive statistics

Standard deviation24.28475177
Coefficient of variation (CV)0.3466776047
Kurtosis17.45286726
Mean70.04995837
Median Absolute Deviation (MAD)11
Skewness2.163569142
Sum84130
Variance589.7491687
MonotonicityNot monotonic
2022-06-07T22:26:21.124509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60143
 
9.8%
7070
 
4.8%
8069
 
4.7%
5057
 
3.9%
7553
 
3.6%
6544
 
3.0%
8540
 
2.7%
7825
 
1.7%
9023
 
1.6%
2123
 
1.6%
Other values (100)654
44.8%
(Missing)259
 
17.7%
ValueCountFrequency (%)
2123
1.6%
2419
1.3%
306
 
0.4%
325
 
0.3%
331
 
0.1%
3410
0.7%
359
 
0.6%
366
 
0.4%
375
 
0.3%
381
 
0.1%
ValueCountFrequency (%)
3132
0.1%
1821
0.1%
1742
0.1%
1681
0.1%
1601
0.1%
1531
0.1%
1521
0.1%
1501
0.1%
1491
0.1%
1441
0.1%

LotArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.82808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:21.240038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.264932
Coefficient of variation (CV)0.949075601
Kurtosis203.243271
Mean10516.82808
Median Absolute Deviation (MAD)1998
Skewness12.20768785
Sum15354569
Variance99625649.65
MonotonicityNot monotonic
2022-06-07T22:26:21.346828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
720025
 
1.7%
960024
 
1.6%
600017
 
1.2%
900014
 
1.0%
840014
 
1.0%
1080014
 
1.0%
168010
 
0.7%
75009
 
0.6%
91008
 
0.5%
81258
 
0.5%
Other values (1063)1317
90.2%
ValueCountFrequency (%)
13001
 
0.1%
14771
 
0.1%
14911
 
0.1%
15261
 
0.1%
15332
 
0.1%
15961
 
0.1%
168010
0.7%
18691
 
0.1%
18902
 
0.1%
19201
 
0.1%
ValueCountFrequency (%)
2152451
0.1%
1646601
0.1%
1590001
0.1%
1151491
0.1%
707611
0.1%
638871
0.1%
572001
0.1%
535041
0.1%
532271
0.1%
531071
0.1%

Street
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Pave
1454 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave1454
99.6%
Grvl6
 
0.4%

Length

2022-06-07T22:26:21.448868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:21.502826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pave1454
99.6%
grvl6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Alley
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)2.2%
Missing1369
Missing (%)93.8%
Memory size11.5 KiB
Grvl
50 
Pave
41 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrvl
2nd rowPave
3rd rowPave
4th rowGrvl
5th rowPave

Common Values

ValueCountFrequency (%)
Grvl50
 
3.4%
Pave41
 
2.8%
(Missing)1369
93.8%

Length

2022-06-07T22:26:21.557826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:21.759254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
grvl50
54.9%
pave41
45.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
925 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg925
63.4%
IR1484
33.2%
IR241
 
2.8%
IR310
 
0.7%

Length

2022-06-07T22:26:21.820253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:21.880329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
reg925
63.4%
ir1484
33.2%
ir241
 
2.8%
ir310
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LandContour
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Lvl
1311 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl1311
89.8%
Bnk63
 
4.3%
HLS50
 
3.4%
Low36
 
2.5%

Length

2022-06-07T22:26:21.949333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.011364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
lvl1311
89.8%
bnk63
 
4.3%
hls50
 
3.4%
low36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Utilities
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
AllPub
1459 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub1459
99.9%
NoSeWa1
 
0.1%

Length

2022-06-07T22:26:22.080379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.133333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
allpub1459
99.9%
nosewa1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LotConfig
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Inside
1052 
Corner
263 
CulDSac
 
94
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.959589041
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside1052
72.1%
Corner263
 
18.0%
CulDSac94
 
6.4%
FR247
 
3.2%
FR34
 
0.3%

Length

2022-06-07T22:26:22.200333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.268331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
inside1052
72.1%
corner263
 
18.0%
culdsac94
 
6.4%
fr247
 
3.2%
fr34
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

LandSlope
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gtl
1382 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl1382
94.7%
Mod65
 
4.5%
Sev13
 
0.9%

Length

2022-06-07T22:26:22.342330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.402333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gtl1382
94.7%
mod65
 
4.5%
sev13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Neighborhood
Categorical

HIGH CORRELATION

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.494520548
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes225
15.4%
CollgCr150
 
10.3%
OldTown113
 
7.7%
Edwards100
 
6.8%
Somerst86
 
5.9%
Gilbert79
 
5.4%
NridgHt77
 
5.3%
Sawyer74
 
5.1%
NWAmes73
 
5.0%
SawyerW59
 
4.0%
Other values (15)424
29.0%

Length

2022-06-07T22:26:22.481335image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names225
15.4%
collgcr150
 
10.3%
oldtown113
 
7.7%
edwards100
 
6.8%
somerst86
 
5.9%
gilbert79
 
5.4%
nridght77
 
5.3%
sawyer74
 
5.1%
nwames73
 
5.0%
sawyerw59
 
4.0%
Other values (15)424
29.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Condition1
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1260 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.121232877
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm1260
86.3%
Feedr81
 
5.5%
Artery48
 
3.3%
RRAn26
 
1.8%
PosN19
 
1.3%
RRAe11
 
0.8%
PosA8
 
0.5%
RRNn5
 
0.3%
RRNe2
 
0.1%

Length

2022-06-07T22:26:22.584333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.658331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
norm1260
86.3%
feedr81
 
5.5%
artery48
 
3.3%
rran26
 
1.8%
posn19
 
1.3%
rrae11
 
0.8%
posa8
 
0.5%
rrnn5
 
0.3%
rrne2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Condition2
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1445 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.006849315
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm1445
99.0%
Feedr6
 
0.4%
Artery2
 
0.1%
RRNn2
 
0.1%
PosN2
 
0.1%
PosA1
 
0.1%
RRAn1
 
0.1%
RRAe1
 
0.1%

Length

2022-06-07T22:26:22.758331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.831378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
norm1445
99.0%
feedr6
 
0.4%
artery2
 
0.1%
rrnn2
 
0.1%
posn2
 
0.1%
posa1
 
0.1%
rran1
 
0.1%
rrae1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BldgType
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.299315068
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam1220
83.6%
TwnhsE114
 
7.8%
Duplex52
 
3.6%
Twnhs43
 
2.9%
2fmCon31
 
2.1%

Length

2022-06-07T22:26:22.922033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:22.986486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1fam1220
83.6%
twnhse114
 
7.8%
duplex52
 
3.6%
twnhs43
 
2.9%
2fmcon31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HouseStyle
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.910958904
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story726
49.7%
2Story445
30.5%
1.5Fin154
 
10.5%
SLvl65
 
4.5%
SFoyer37
 
2.5%
1.5Unf14
 
1.0%
2.5Unf11
 
0.8%
2.5Fin8
 
0.5%

Length

2022-06-07T22:26:23.072480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:23.144494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1story726
49.7%
2story445
30.5%
1.5fin154
 
10.5%
slvl65
 
4.5%
sfoyer37
 
2.5%
1.5unf14
 
1.0%
2.5unf11
 
0.8%
2.5fin8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

OverallQual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.099315068
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:23.230450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.382996547
Coefficient of variation (CV)0.2267462053
Kurtosis0.09629277836
Mean6.099315068
Median Absolute Deviation (MAD)1
Skewness0.2169439278
Sum8905
Variance1.912679448
MonotonicityNot monotonic
2022-06-07T22:26:23.306494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
4116
 
7.9%
943
 
2.9%
320
 
1.4%
1018
 
1.2%
23
 
0.2%
12
 
0.1%
ValueCountFrequency (%)
12
 
0.1%
23
 
0.2%
320
 
1.4%
4116
 
7.9%
5397
27.2%
6374
25.6%
7319
21.8%
8168
11.5%
943
 
2.9%
1018
 
1.2%
ValueCountFrequency (%)
1018
 
1.2%
943
 
2.9%
8168
11.5%
7319
21.8%
6374
25.6%
5397
27.2%
4116
 
7.9%
320
 
1.4%
23
 
0.2%
12
 
0.1%

OverallCond
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.575342466
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:23.379921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.112799337
Coefficient of variation (CV)0.1995930014
Kurtosis1.106413461
Mean5.575342466
Median Absolute Deviation (MAD)0
Skewness0.6930674725
Sum8140
Variance1.238322364
MonotonicityNot monotonic
2022-06-07T22:26:23.456424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
457
 
3.9%
325
 
1.7%
922
 
1.5%
25
 
0.3%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
25
 
0.3%
325
 
1.7%
457
 
3.9%
5821
56.2%
6252
 
17.3%
7205
 
14.0%
872
 
4.9%
922
 
1.5%
ValueCountFrequency (%)
922
 
1.5%
872
 
4.9%
7205
 
14.0%
6252
 
17.3%
5821
56.2%
457
 
3.9%
325
 
1.7%
25
 
0.3%
11
 
0.1%

YearBuilt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.267808
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:23.645612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.20290404
Coefficient of variation (CV)0.01532156307
Kurtosis-0.4395519416
Mean1971.267808
Median Absolute Deviation (MAD)25
Skewness-0.6134611725
Sum2878051
Variance912.2154126
MonotonicityNot monotonic
2022-06-07T22:26:23.752860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200667
 
4.6%
200564
 
4.4%
200454
 
3.7%
200749
 
3.4%
200345
 
3.1%
197633
 
2.3%
197732
 
2.2%
192030
 
2.1%
195926
 
1.8%
199825
 
1.7%
Other values (102)1035
70.9%
ValueCountFrequency (%)
18721
 
0.1%
18751
 
0.1%
18804
 
0.3%
18821
 
0.1%
18852
 
0.1%
18902
 
0.1%
18922
 
0.1%
18931
 
0.1%
18981
 
0.1%
190010
0.7%
ValueCountFrequency (%)
20101
 
0.1%
200918
 
1.2%
200823
 
1.6%
200749
3.4%
200667
4.6%
200564
4.4%
200454
3.7%
200345
3.1%
200223
 
1.6%
200120
 
1.4%

YearRemodAdd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.865753
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:23.865303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.64540681
Coefficient of variation (CV)0.01040141217
Kurtosis-1.272245192
Mean1984.865753
Median Absolute Deviation (MAD)13
Skewness-0.5035620027
Sum2897904
Variance426.2328223
MonotonicityNot monotonic
2022-06-07T22:26:23.974301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950178
 
12.2%
200697
 
6.6%
200776
 
5.2%
200573
 
5.0%
200462
 
4.2%
200055
 
3.8%
200351
 
3.5%
200248
 
3.3%
200840
 
2.7%
199636
 
2.5%
Other values (51)744
51.0%
ValueCountFrequency (%)
1950178
12.2%
19514
 
0.3%
19525
 
0.3%
195310
 
0.7%
195414
 
1.0%
19559
 
0.6%
195610
 
0.7%
19579
 
0.6%
195815
 
1.0%
195918
 
1.2%
ValueCountFrequency (%)
20106
 
0.4%
200923
 
1.6%
200840
2.7%
200776
5.2%
200697
6.6%
200573
5.0%
200462
4.2%
200351
3.5%
200248
3.3%
200121
 
1.4%

RoofStyle
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.62260274
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable1141
78.2%
Hip286
 
19.6%
Flat13
 
0.9%
Gambrel11
 
0.8%
Mansard7
 
0.5%
Shed2
 
0.1%

Length

2022-06-07T22:26:24.098307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:24.162341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gable1141
78.2%
hip286
 
19.6%
flat13
 
0.9%
gambrel11
 
0.8%
mansard7
 
0.5%
shed2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

RoofMatl
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
CompShg
1434 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.996575342
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg1434
98.2%
Tar&Grv11
 
0.8%
WdShngl6
 
0.4%
WdShake5
 
0.3%
Metal1
 
0.1%
Membran1
 
0.1%
Roll1
 
0.1%
ClyTile1
 
0.1%

Length

2022-06-07T22:26:24.240353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:24.306391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
compshg1434
98.2%
tar&grv11
 
0.8%
wdshngl6
 
0.4%
wdshake5
 
0.3%
metal1
 
0.1%
membran1
 
0.1%
roll1
 
0.1%
clytile1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Exterior1st
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.979452055
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd515
35.3%
HdBoard222
15.2%
MetalSd220
15.1%
Wd Sdng206
 
14.1%
Plywood108
 
7.4%
CemntBd61
 
4.2%
BrkFace50
 
3.4%
WdShing26
 
1.8%
Stucco25
 
1.7%
AsbShng20
 
1.4%
Other values (5)7
 
0.5%

Length

2022-06-07T22:26:24.400353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd515
30.9%
hdboard222
13.3%
metalsd220
13.2%
wd206
 
12.4%
sdng206
 
12.4%
plywood108
 
6.5%
cemntbd61
 
3.7%
brkface50
 
3.0%
wdshing26
 
1.6%
stucco25
 
1.5%
Other values (6)27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Exterior2nd
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
504 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.973287671
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd504
34.5%
MetalSd214
14.7%
HdBoard207
14.2%
Wd Sdng197
 
13.5%
Plywood142
 
9.7%
CmentBd60
 
4.1%
Wd Shng38
 
2.6%
Stucco26
 
1.8%
BrkFace25
 
1.7%
AsbShng20
 
1.4%
Other values (6)27
 
1.8%

Length

2022-06-07T22:26:24.506354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd504
29.6%
wd235
13.8%
metalsd214
12.6%
hdboard207
12.2%
sdng197
 
11.6%
plywood142
 
8.3%
cmentbd60
 
3.5%
shng38
 
2.2%
stucco26
 
1.5%
brkface25
 
1.5%
Other values (8)54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MasVnrType
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing8
Missing (%)0.5%
Memory size11.5 KiB
None
864 
BrkFace
445 
Stone
128 
BrkCmn
 
15

Length

Max length7
Median length4
Mean length5.028236915
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNone
3rd rowBrkFace
4th rowNone
5th rowBrkFace

Common Values

ValueCountFrequency (%)
None864
59.2%
BrkFace445
30.5%
Stone128
 
8.8%
BrkCmn15
 
1.0%
(Missing)8
 
0.5%

Length

2022-06-07T22:26:24.600468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:24.657506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
none864
59.5%
brkface445
30.6%
stone128
 
8.8%
brkcmn15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MasVnrArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct327
Distinct (%)22.5%
Missing8
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean103.6852617
Minimum0
Maximum1600
Zeros861
Zeros (%)59.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:24.740506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.0662066
Coefficient of variation (CV)1.746306115
Kurtosis10.08241732
Mean103.6852617
Median Absolute Deviation (MAD)0
Skewness2.66908421
Sum150551
Variance32784.97117
MonotonicityNot monotonic
2022-06-07T22:26:24.851466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0861
59.0%
728
 
0.5%
1088
 
0.5%
1808
 
0.5%
1207
 
0.5%
167
 
0.5%
3406
 
0.4%
1066
 
0.4%
806
 
0.4%
2006
 
0.4%
Other values (317)529
36.2%
(Missing)8
 
0.5%
ValueCountFrequency (%)
0861
59.0%
12
 
0.1%
111
 
0.1%
141
 
0.1%
167
 
0.5%
182
 
0.1%
221
 
0.1%
241
 
0.1%
271
 
0.1%
281
 
0.1%
ValueCountFrequency (%)
16001
0.1%
13781
0.1%
11701
0.1%
11291
0.1%
11151
0.1%
10471
0.1%
10311
0.1%
9751
0.1%
9221
0.1%
9211
0.1%

ExterQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA906
62.1%
Gd488
33.4%
Ex52
 
3.6%
Fa14
 
1.0%

Length

2022-06-07T22:26:24.954481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.010930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta906
62.1%
gd488
33.4%
ex52
 
3.6%
fa14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ExterCond
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1282
87.8%
Gd146
 
10.0%
Fa28
 
1.9%
Ex3
 
0.2%
Po1
 
0.1%

Length

2022-06-07T22:26:25.075431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.128576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta1282
87.8%
gd146
 
10.0%
fa28
 
1.9%
ex3
 
0.2%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Foundation
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.515753425
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc647
44.3%
CBlock634
43.4%
BrkTil146
 
10.0%
Slab24
 
1.6%
Stone6
 
0.4%
Wood3
 
0.2%

Length

2022-06-07T22:26:25.202576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.275575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pconc647
44.3%
cblock634
43.4%
brktil146
 
10.0%
slab24
 
1.6%
stone6
 
0.4%
wood3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtQual
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
649 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA649
44.5%
Gd618
42.3%
Ex121
 
8.3%
Fa35
 
2.4%
(Missing)37
 
2.5%

Length

2022-06-07T22:26:25.446576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.509579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta649
45.6%
gd618
43.4%
ex121
 
8.5%
fa35
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtCond
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
1311 
Gd
 
65
Fa
 
45
Po
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA1311
89.8%
Gd65
 
4.5%
Fa45
 
3.1%
Po2
 
0.1%
(Missing)37
 
2.5%

Length

2022-06-07T22:26:25.584575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.643577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta1311
92.1%
gd65
 
4.6%
fa45
 
3.2%
po2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtExposure
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.3%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
No
953 
Av
221 
Gd
134 
Mn
114 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No953
65.3%
Av221
 
15.1%
Gd134
 
9.2%
Mn114
 
7.8%
(Missing)38
 
2.6%

Length

2022-06-07T22:26:25.708058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.761378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no953
67.0%
av221
 
15.5%
gd134
 
9.4%
mn114
 
8.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtFinType1
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
Unf
430 
GLQ
418 
ALQ
220 
BLQ
148 
Rec
133 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf430
29.5%
GLQ418
28.6%
ALQ220
15.1%
BLQ148
 
10.1%
Rec133
 
9.1%
LwQ74
 
5.1%
(Missing)37
 
2.5%

Length

2022-06-07T22:26:25.823823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:25.878212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unf430
30.2%
glq418
29.4%
alq220
15.5%
blq148
 
10.4%
rec133
 
9.3%
lwq74
 
5.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtFinSF1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.639726
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:25.961192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.0980908
Coefficient of variation (CV)1.028082167
Kurtosis11.11823629
Mean443.639726
Median Absolute Deviation (MAD)383.5
Skewness1.685503072
Sum647714
Variance208025.4685
MonotonicityNot monotonic
2022-06-07T22:26:26.060055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0467
32.0%
2412
 
0.8%
169
 
0.6%
6865
 
0.3%
6625
 
0.3%
205
 
0.3%
9365
 
0.3%
6165
 
0.3%
5604
 
0.3%
5534
 
0.3%
Other values (627)939
64.3%
ValueCountFrequency (%)
0467
32.0%
21
 
0.1%
169
 
0.6%
205
 
0.3%
2412
 
0.8%
251
 
0.1%
271
 
0.1%
283
 
0.2%
331
 
0.1%
351
 
0.1%
ValueCountFrequency (%)
56441
0.1%
22601
0.1%
21881
0.1%
20961
0.1%
19041
0.1%
18801
0.1%
18101
0.1%
17671
0.1%
17211
0.1%
16961
0.1%

BsmtFinType2
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing38
Missing (%)2.6%
Memory size11.5 KiB
Unf
1256 
Rec
 
54
LwQ
 
46
BLQ
 
33
ALQ
 
19

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf1256
86.0%
Rec54
 
3.7%
LwQ46
 
3.2%
BLQ33
 
2.3%
ALQ19
 
1.3%
GLQ14
 
1.0%
(Missing)38
 
2.6%

Length

2022-06-07T22:26:26.153107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:26.207062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unf1256
88.3%
rec54
 
3.8%
lwq46
 
3.2%
blq33
 
2.3%
alq19
 
1.3%
glq14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtFinSF2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.54931507
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:26.287061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.3192728
Coefficient of variation (CV)3.465556315
Kurtosis20.11333755
Mean46.54931507
Median Absolute Deviation (MAD)0
Skewness4.255261109
Sum67962
Variance26023.90778
MonotonicityNot monotonic
2022-06-07T22:26:26.390360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01293
88.6%
1805
 
0.3%
3743
 
0.2%
5512
 
0.1%
1472
 
0.1%
2942
 
0.1%
3912
 
0.1%
5392
 
0.1%
962
 
0.1%
4802
 
0.1%
Other values (134)145
 
9.9%
ValueCountFrequency (%)
01293
88.6%
281
 
0.1%
321
 
0.1%
351
 
0.1%
401
 
0.1%
412
 
0.1%
642
 
0.1%
681
 
0.1%
801
 
0.1%
811
 
0.1%
ValueCountFrequency (%)
14741
0.1%
11271
0.1%
11201
0.1%
10851
0.1%
10801
0.1%
10631
0.1%
10611
0.1%
10571
0.1%
10311
0.1%
10291
0.1%

BsmtUnfSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.240411
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:26.504170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.8669553
Coefficient of variation (CV)0.7789765094
Kurtosis0.4749939878
Mean567.240411
Median Absolute Deviation (MAD)288
Skewness0.9202684528
Sum828171
Variance195246.4062
MonotonicityNot monotonic
2022-06-07T22:26:26.610861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0118
 
8.1%
7289
 
0.6%
3848
 
0.5%
6007
 
0.5%
3007
 
0.5%
5727
 
0.5%
2706
 
0.4%
6256
 
0.4%
6726
 
0.4%
4406
 
0.4%
Other values (770)1280
87.7%
ValueCountFrequency (%)
0118
8.1%
141
 
0.1%
151
 
0.1%
232
 
0.1%
261
 
0.1%
291
 
0.1%
301
 
0.1%
322
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
23361
0.1%
21531
0.1%
21211
0.1%
20461
0.1%
20421
0.1%
20021
0.1%
19691
0.1%
19351
0.1%
19261
0.1%
19071
0.1%

TotalBsmtSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.429452
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:26.719143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.7053245
Coefficient of variation (CV)0.4148790481
Kurtosis13.25048328
Mean1057.429452
Median Absolute Deviation (MAD)234.5
Skewness1.524254549
Sum1543847
Variance192462.3617
MonotonicityNot monotonic
2022-06-07T22:26:26.823444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
037
 
2.5%
86435
 
2.4%
67217
 
1.2%
91215
 
1.0%
104014
 
1.0%
81613
 
0.9%
76812
 
0.8%
72812
 
0.8%
89411
 
0.8%
78011
 
0.8%
Other values (711)1283
87.9%
ValueCountFrequency (%)
037
2.5%
1051
 
0.1%
1901
 
0.1%
2643
 
0.2%
2701
 
0.1%
2901
 
0.1%
3191
 
0.1%
3601
 
0.1%
3721
 
0.1%
3847
 
0.5%
ValueCountFrequency (%)
61101
0.1%
32061
0.1%
32001
0.1%
31381
0.1%
30941
0.1%
26331
0.1%
25241
0.1%
24441
0.1%
23961
0.1%
23921
0.1%

Heating
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GasA
1428 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.000684932
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA1428
97.8%
GasW18
 
1.2%
Grav7
 
0.5%
Wall4
 
0.3%
OthW2
 
0.1%
Floor1
 
0.1%

Length

2022-06-07T22:26:26.923126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:26.980163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gasa1428
97.8%
gasw18
 
1.2%
grav7
 
0.5%
wall4
 
0.3%
othw2
 
0.1%
floor1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HeatingQC
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex741
50.8%
TA428
29.3%
Gd241
 
16.5%
Fa49
 
3.4%
Po1
 
0.1%

Length

2022-06-07T22:26:27.140073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:27.196582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ex741
50.8%
ta428
29.3%
gd241
 
16.5%
fa49
 
3.4%
po1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CentralAir
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True1365
93.5%
False95
 
6.5%
2022-06-07T22:26:27.238583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Electrical
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
SBrkr
1334 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.998629198
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr1334
91.4%
FuseA94
 
6.4%
FuseF27
 
1.8%
FuseP3
 
0.2%
Mix1
 
0.1%
(Missing)1
 
0.1%

Length

2022-06-07T22:26:27.299632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:27.360585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr1334
91.4%
fusea94
 
6.4%
fusef27
 
1.9%
fusep3
 
0.2%
mix1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1stFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.626712
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:27.437509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.587738
Coefficient of variation (CV)0.3325123481
Kurtosis5.745841482
Mean1162.626712
Median Absolute Deviation (MAD)234.5
Skewness1.376756622
Sum1697435
Variance149450.0792
MonotonicityNot monotonic
2022-06-07T22:26:27.537922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86425
 
1.7%
104016
 
1.1%
91214
 
1.0%
89412
 
0.8%
84812
 
0.8%
67211
 
0.8%
6309
 
0.6%
8169
 
0.6%
4837
 
0.5%
9607
 
0.5%
Other values (743)1338
91.6%
ValueCountFrequency (%)
3341
 
0.1%
3721
 
0.1%
4381
 
0.1%
4801
 
0.1%
4837
0.5%
4951
 
0.1%
5205
0.3%
5251
 
0.1%
5261
 
0.1%
5361
 
0.1%
ValueCountFrequency (%)
46921
0.1%
32281
0.1%
31381
0.1%
28981
0.1%
26331
0.1%
25241
0.1%
25151
0.1%
24441
0.1%
24111
0.1%
24021
0.1%

2ndFlrSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.9924658
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:27.649897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.5284359
Coefficient of variation (CV)1.258034335
Kurtosis-0.5534635576
Mean346.9924658
Median Absolute Deviation (MAD)0
Skewness0.8130298163
Sum506609
Variance190557.0753
MonotonicityNot monotonic
2022-06-07T22:26:27.755939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0829
56.8%
72810
 
0.7%
5049
 
0.6%
5468
 
0.5%
6728
 
0.5%
6007
 
0.5%
7207
 
0.5%
8966
 
0.4%
8625
 
0.3%
7805
 
0.3%
Other values (407)566
38.8%
ValueCountFrequency (%)
0829
56.8%
1101
 
0.1%
1671
 
0.1%
1921
 
0.1%
2081
 
0.1%
2131
 
0.1%
2201
 
0.1%
2241
 
0.1%
2402
 
0.1%
2522
 
0.1%
ValueCountFrequency (%)
20651
0.1%
18721
0.1%
18181
0.1%
17961
0.1%
16111
0.1%
15891
0.1%
15401
0.1%
15381
0.1%
15231
0.1%
15191
0.1%

LowQualFinSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.844520548
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:27.848895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.62308143
Coefficient of variation (CV)8.319430317
Kurtosis83.23481667
Mean5.844520548
Median Absolute Deviation (MAD)0
Skewness9.011341288
Sum8533
Variance2364.204048
MonotonicityNot monotonic
2022-06-07T22:26:27.928687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
01434
98.2%
803
 
0.2%
3602
 
0.1%
2051
 
0.1%
4791
 
0.1%
3971
 
0.1%
5141
 
0.1%
1201
 
0.1%
4811
 
0.1%
2321
 
0.1%
Other values (14)14
 
1.0%
ValueCountFrequency (%)
01434
98.2%
531
 
0.1%
803
 
0.2%
1201
 
0.1%
1441
 
0.1%
1561
 
0.1%
2051
 
0.1%
2321
 
0.1%
2341
 
0.1%
3602
 
0.1%
ValueCountFrequency (%)
5721
0.1%
5281
0.1%
5151
0.1%
5141
0.1%
5131
0.1%
4811
0.1%
4791
0.1%
4731
0.1%
4201
0.1%
3971
0.1%

GrLivArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.463699
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:28.022135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.4803834
Coefficient of variation (CV)0.3467456092
Kurtosis4.895120581
Mean1515.463699
Median Absolute Deviation (MAD)326
Skewness1.366560356
Sum2212577
Variance276129.6334
MonotonicityNot monotonic
2022-06-07T22:26:28.128341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86422
 
1.5%
104014
 
1.0%
89411
 
0.8%
145610
 
0.7%
84810
 
0.7%
12009
 
0.6%
9129
 
0.6%
8168
 
0.5%
10928
 
0.5%
17287
 
0.5%
Other values (851)1352
92.6%
ValueCountFrequency (%)
3341
 
0.1%
4381
 
0.1%
4801
 
0.1%
5201
 
0.1%
6051
 
0.1%
6161
 
0.1%
6306
0.4%
6722
 
0.1%
6911
 
0.1%
6931
 
0.1%
ValueCountFrequency (%)
56421
0.1%
46761
0.1%
44761
0.1%
43161
0.1%
36271
0.1%
36081
0.1%
34931
0.1%
34471
0.1%
33951
0.1%
32791
0.1%

BsmtFullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Length

2022-06-07T22:26:28.224296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:28.276465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0856
58.6%
1588
40.3%
215
 
1.0%
31
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BsmtHalfBath
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Length

2022-06-07T22:26:28.336419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:28.387466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01378
94.4%
180
 
5.5%
22
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FullBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Length

2022-06-07T22:26:28.443466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:28.496018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2768
52.6%
1650
44.5%
333
 
2.3%
09
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

HalfBath
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Length

2022-06-07T22:26:28.557017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:28.608068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0913
62.5%
1535
36.6%
212
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BedroomAbvGr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.866438356
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:28.748021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8157780441
Coefficient of variation (CV)0.2845964025
Kurtosis2.230874582
Mean2.866438356
Median Absolute Deviation (MAD)0
Skewness0.2117900963
Sum4185
Variance0.6654938173
MonotonicityNot monotonic
2022-06-07T22:26:28.820842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3804
55.1%
2358
24.5%
4213
 
14.6%
150
 
3.4%
521
 
1.4%
67
 
0.5%
06
 
0.4%
81
 
0.1%
ValueCountFrequency (%)
06
 
0.4%
150
 
3.4%
2358
24.5%
3804
55.1%
4213
 
14.6%
521
 
1.4%
67
 
0.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
67
 
0.5%
521
 
1.4%
4213
 
14.6%
3804
55.1%
2358
24.5%
150
 
3.4%
06
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Length

2022-06-07T22:26:28.905849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:28.968395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
11392
95.3%
265
 
4.5%
32
 
0.1%
01
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

KitchenQual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA735
50.3%
Gd586
40.1%
Ex100
 
6.8%
Fa39
 
2.7%

Length

2022-06-07T22:26:29.041350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:29.104387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta735
50.3%
gd586
40.1%
ex100
 
6.8%
fa39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

TotRmsAbvGrd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.517808219
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:29.164355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.625393291
Coefficient of variation (CV)0.2493772808
Kurtosis0.8807615657
Mean6.517808219
Median Absolute Deviation (MAD)1
Skewness0.6763408364
Sum9516
Variance2.641903349
MonotonicityNot monotonic
2022-06-07T22:26:29.247350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6402
27.5%
7329
22.5%
5275
18.8%
8187
12.8%
497
 
6.6%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
317
 
1.2%
1211
 
0.8%
Other values (2)2
 
0.1%
ValueCountFrequency (%)
21
 
0.1%
317
 
1.2%
497
 
6.6%
5275
18.8%
6402
27.5%
7329
22.5%
8187
12.8%
975
 
5.1%
1047
 
3.2%
1118
 
1.2%
ValueCountFrequency (%)
141
 
0.1%
1211
 
0.8%
1118
 
1.2%
1047
 
3.2%
975
 
5.1%
8187
12.8%
7329
22.5%
6402
27.5%
5275
18.8%
497
 
6.6%

Functional
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Typ
1360 
Min2
 
34
Min1
 
31
Mod
 
15
Maj1
 
14
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.057534247
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ1360
93.2%
Min234
 
2.3%
Min131
 
2.1%
Mod15
 
1.0%
Maj114
 
1.0%
Maj25
 
0.3%
Sev1
 
0.1%

Length

2022-06-07T22:26:29.334603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:29.394603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
typ1360
93.2%
min234
 
2.3%
min131
 
2.1%
mod15
 
1.0%
maj114
 
1.0%
maj25
 
0.3%
sev1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fireplaces
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Length

2022-06-07T22:26:29.470600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:29.522629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0690
47.3%
1650
44.5%
2115
 
7.9%
35
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FireplaceQu
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.6%
Missing690
Missing (%)47.3%
Memory size11.5 KiB
Gd
380 
TA
313 
Fa
 
33
Ex
 
24
Po
 
20

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
Gd380
26.0%
TA313
21.4%
Fa33
 
2.3%
Ex24
 
1.6%
Po20
 
1.4%
(Missing)690
47.3%

Length

2022-06-07T22:26:29.584602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:29.637652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gd380
49.4%
ta313
40.6%
fa33
 
4.3%
ex24
 
3.1%
po20
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageType
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
Attchd
870 
Detchd
387 
BuiltIn
88 
Basment
 
19
CarPort
 
9

Length

Max length7
Median length6
Mean length6.084118927
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd870
59.6%
Detchd387
26.5%
BuiltIn88
 
6.0%
Basment19
 
1.3%
CarPort9
 
0.6%
2Types6
 
0.4%
(Missing)81
 
5.5%

Length

2022-06-07T22:26:29.703601image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:29.760399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
attchd870
63.1%
detchd387
28.1%
builtin88
 
6.4%
basment19
 
1.4%
carport9
 
0.7%
2types6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageYrBlt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct97
Distinct (%)7.0%
Missing81
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean1978.506164
Minimum1900
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:29.845349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1930
Q11961
median1980
Q32002
95-th percentile2007
Maximum2010
Range110
Interquartile range (IQR)41

Descriptive statistics

Standard deviation24.68972477
Coefficient of variation (CV)0.01247897288
Kurtosis-0.418340998
Mean1978.506164
Median Absolute Deviation (MAD)21
Skewness-0.6494146239
Sum2728360
Variance609.5825091
MonotonicityNot monotonic
2022-06-07T22:26:29.949271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200565
 
4.5%
200659
 
4.0%
200453
 
3.6%
200350
 
3.4%
200749
 
3.4%
197735
 
2.4%
199831
 
2.1%
199930
 
2.1%
197629
 
2.0%
200829
 
2.0%
Other values (87)949
65.0%
(Missing)81
 
5.5%
ValueCountFrequency (%)
19001
 
0.1%
19061
 
0.1%
19081
 
0.1%
19103
 
0.2%
19142
 
0.1%
19152
 
0.1%
19165
 
0.3%
19182
 
0.1%
192014
1.0%
19213
 
0.2%
ValueCountFrequency (%)
20103
 
0.2%
200921
 
1.4%
200829
2.0%
200749
3.4%
200659
4.0%
200565
4.5%
200453
3.6%
200350
3.4%
200226
 
1.8%
200120
 
1.4%

GarageFinish
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.2%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
Unf
605 
RFn
422 
Fin
352 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf605
41.4%
RFn422
28.9%
Fin352
24.1%
(Missing)81
 
5.5%

Length

2022-06-07T22:26:30.043270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:30.095279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
unf605
43.9%
rfn422
30.6%
fin352
25.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageCars
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Length

2022-06-07T22:26:30.160321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:30.220322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2824
56.4%
1369
25.3%
3181
 
12.4%
081
 
5.5%
45
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.980137
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:30.298628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.8048415
Coefficient of variation (CV)0.452037675
Kurtosis0.9170672023
Mean472.980137
Median Absolute Deviation (MAD)120
Skewness0.1799809067
Sum690551
Variance45712.51023
MonotonicityNot monotonic
2022-06-07T22:26:30.487998image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
081
 
5.5%
44049
 
3.4%
57647
 
3.2%
24038
 
2.6%
48434
 
2.3%
52833
 
2.3%
28827
 
1.8%
40025
 
1.7%
26424
 
1.6%
48024
 
1.6%
Other values (431)1078
73.8%
ValueCountFrequency (%)
081
5.5%
1602
 
0.1%
1641
 
0.1%
1809
 
0.6%
1861
 
0.1%
1891
 
0.1%
1921
 
0.1%
1981
 
0.1%
2004
 
0.3%
2053
 
0.2%
ValueCountFrequency (%)
14181
0.1%
13901
0.1%
13561
0.1%
12481
0.1%
12201
0.1%
11661
0.1%
11341
0.1%
10691
0.1%
10531
0.1%
10522
0.1%

GarageQual
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
TA
1311 
Fa
 
48
Gd
 
14
Ex
 
3
Po
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1311
89.8%
Fa48
 
3.3%
Gd14
 
1.0%
Ex3
 
0.2%
Po3
 
0.2%
(Missing)81
 
5.5%

Length

2022-06-07T22:26:30.584120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:30.638167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta1311
95.1%
fa48
 
3.5%
gd14
 
1.0%
ex3
 
0.2%
po3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

GarageCond
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
TA
1326 
Fa
 
35
Gd
 
9
Po
 
7
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA1326
90.8%
Fa35
 
2.4%
Gd9
 
0.6%
Po7
 
0.5%
Ex2
 
0.1%
(Missing)81
 
5.5%

Length

2022-06-07T22:26:30.706206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:30.762214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta1326
96.2%
fa35
 
2.5%
gd9
 
0.7%
po7
 
0.5%
ex2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PavedDrive
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Y
1340 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y1340
91.8%
N90
 
6.2%
P30
 
2.1%

Length

2022-06-07T22:26:30.830167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:30.883148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
y1340
91.8%
n90
 
6.2%
p30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

WoodDeckSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.24452055
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:30.956147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.3387944
Coefficient of variation (CV)1.329931901
Kurtosis2.992950925
Mean94.24452055
Median Absolute Deviation (MAD)0
Skewness1.541375757
Sum137597
Variance15709.81337
MonotonicityNot monotonic
2022-06-07T22:26:31.062091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0761
52.1%
19238
 
2.6%
10036
 
2.5%
14433
 
2.3%
12031
 
2.1%
16828
 
1.9%
14015
 
1.0%
22414
 
1.0%
20810
 
0.7%
24010
 
0.7%
Other values (264)484
33.2%
ValueCountFrequency (%)
0761
52.1%
122
 
0.1%
242
 
0.1%
262
 
0.1%
282
 
0.1%
301
 
0.1%
321
 
0.1%
331
 
0.1%
351
 
0.1%
364
 
0.3%
ValueCountFrequency (%)
8571
0.1%
7361
0.1%
7281
0.1%
6701
0.1%
6681
0.1%
6351
0.1%
5861
0.1%
5761
0.1%
5741
0.1%
5501
0.1%

OpenPorchSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.66027397
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:31.168597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.25602768
Coefficient of variation (CV)1.419966538
Kurtosis8.490335806
Mean46.66027397
Median Absolute Deviation (MAD)25
Skewness2.36434174
Sum68124
Variance4389.861203
MonotonicityNot monotonic
2022-06-07T22:26:31.270556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0656
44.9%
3629
 
2.0%
4822
 
1.5%
2021
 
1.4%
4019
 
1.3%
4519
 
1.3%
2416
 
1.1%
3016
 
1.1%
6015
 
1.0%
3914
 
1.0%
Other values (192)633
43.4%
ValueCountFrequency (%)
0656
44.9%
41
 
0.1%
81
 
0.1%
101
 
0.1%
111
 
0.1%
123
 
0.2%
151
 
0.1%
168
 
0.5%
172
 
0.1%
185
 
0.3%
ValueCountFrequency (%)
5471
0.1%
5231
0.1%
5021
0.1%
4181
0.1%
4061
0.1%
3641
0.1%
3411
0.1%
3191
0.1%
3122
0.1%
3041
0.1%

EnclosedPorch
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95410959
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:31.380140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.1191486
Coefficient of variation (CV)2.783950237
Kurtosis10.43076594
Mean21.95410959
Median Absolute Deviation (MAD)0
Skewness3.089871904
Sum32053
Variance3735.550326
MonotonicityNot monotonic
2022-06-07T22:26:31.483146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01252
85.8%
11215
 
1.0%
966
 
0.4%
1925
 
0.3%
1445
 
0.3%
1205
 
0.3%
2165
 
0.3%
1564
 
0.3%
1164
 
0.3%
2524
 
0.3%
Other values (110)155
 
10.6%
ValueCountFrequency (%)
01252
85.8%
191
 
0.1%
201
 
0.1%
241
 
0.1%
301
 
0.1%
322
 
0.1%
342
 
0.1%
362
 
0.1%
371
 
0.1%
392
 
0.1%
ValueCountFrequency (%)
5521
0.1%
3861
0.1%
3301
0.1%
3181
0.1%
3011
0.1%
2941
0.1%
2931
0.1%
2911
0.1%
2861
0.1%
2801
0.1%

3SsnPorch
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589041
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:31.574513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.31733056
Coefficient of variation (CV)8.598493896
Kurtosis123.6623794
Mean3.409589041
Median Absolute Deviation (MAD)0
Skewness10.30434203
Sum4978
Variance859.505871
MonotonicityNot monotonic
2022-06-07T22:26:31.658297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
01436
98.4%
1683
 
0.2%
1442
 
0.1%
1802
 
0.1%
2162
 
0.1%
2901
 
0.1%
1531
 
0.1%
961
 
0.1%
231
 
0.1%
1621
 
0.1%
Other values (10)10
 
0.7%
ValueCountFrequency (%)
01436
98.4%
231
 
0.1%
961
 
0.1%
1301
 
0.1%
1401
 
0.1%
1442
 
0.1%
1531
 
0.1%
1621
 
0.1%
1683
 
0.2%
1802
 
0.1%
ValueCountFrequency (%)
5081
0.1%
4071
0.1%
3201
0.1%
3041
0.1%
2901
0.1%
2451
0.1%
2381
0.1%
2162
0.1%
1961
0.1%
1821
0.1%

ScreenPorch
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.0609589
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:31.754523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.75741528
Coefficient of variation (CV)3.70211589
Kurtosis18.43906784
Mean15.0609589
Median Absolute Deviation (MAD)0
Skewness4.122213743
Sum21989
Variance3108.889359
MonotonicityNot monotonic
2022-06-07T22:26:31.870915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01344
92.1%
1926
 
0.4%
1205
 
0.3%
2245
 
0.3%
1894
 
0.3%
1804
 
0.3%
1473
 
0.2%
903
 
0.2%
1603
 
0.2%
1443
 
0.2%
Other values (66)80
 
5.5%
ValueCountFrequency (%)
01344
92.1%
401
 
0.1%
531
 
0.1%
601
 
0.1%
631
 
0.1%
801
 
0.1%
903
 
0.2%
951
 
0.1%
991
 
0.1%
1002
 
0.1%
ValueCountFrequency (%)
4801
0.1%
4401
0.1%
4101
0.1%
3961
0.1%
3851
0.1%
3741
0.1%
3221
0.1%
3121
0.1%
2911
0.1%
2882
0.1%

PoolArea
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.75890411
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:31.960514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.17730694
Coefficient of variation (CV)14.56277759
Kurtosis223.2684989
Mean2.75890411
Median Absolute Deviation (MAD)0
Skewness14.82837364
Sum4028
Variance1614.215993
MonotonicityNot monotonic
2022-06-07T22:26:32.031425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
01453
99.5%
5121
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
4801
 
0.1%
5191
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
01453
99.5%
4801
 
0.1%
5121
 
0.1%
5191
 
0.1%
5551
 
0.1%
5761
 
0.1%
6481
 
0.1%
7381
 
0.1%
ValueCountFrequency (%)
7381
 
0.1%
6481
 
0.1%
5761
 
0.1%
5551
 
0.1%
5191
 
0.1%
5121
 
0.1%
4801
 
0.1%
01453
99.5%

PoolQC
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)42.9%
Missing1453
Missing (%)99.5%
Memory size11.5 KiB
Gd
Ex
Fa

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEx
2nd rowFa
3rd rowGd
4th rowEx
5th rowGd

Common Values

ValueCountFrequency (%)
Gd3
 
0.2%
Ex2
 
0.1%
Fa2
 
0.1%
(Missing)1453
99.5%

Length

2022-06-07T22:26:32.199918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:32.249918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gd3
42.9%
ex2
28.6%
fa2
28.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Fence
Categorical

MISSING

Distinct4
Distinct (%)1.4%
Missing1179
Missing (%)80.8%
Memory size11.5 KiB
MnPrv
157 
GdPrv
59 
GdWo
54 
MnWw
 
11

Length

Max length5
Median length5
Mean length4.768683274
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMnPrv
2nd rowGdWo
3rd rowGdPrv
4th rowMnPrv
5th rowGdPrv

Common Values

ValueCountFrequency (%)
MnPrv157
 
10.8%
GdPrv59
 
4.0%
GdWo54
 
3.7%
MnWw11
 
0.8%
(Missing)1179
80.8%

Length

2022-06-07T22:26:32.304830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:32.355189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mnprv157
55.9%
gdprv59
 
21.0%
gdwo54
 
19.2%
mnww11
 
3.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MiscFeature
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)7.4%
Missing1406
Missing (%)96.3%
Memory size11.5 KiB
Shed
49 
Gar2
 
2
Othr
 
2
TenC
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st rowShed
2nd rowShed
3rd rowShed
4th rowShed
5th rowShed

Common Values

ValueCountFrequency (%)
Shed49
 
3.4%
Gar22
 
0.1%
Othr2
 
0.1%
TenC1
 
0.1%
(Missing)1406
96.3%

Length

2022-06-07T22:26:32.415226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:32.465279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
shed49
90.7%
gar22
 
3.7%
othr2
 
3.7%
tenc1
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MiscVal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.4890411
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:32.528279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.1230245
Coefficient of variation (CV)11.408001
Kurtosis701.0033423
Mean43.4890411
Median Absolute Deviation (MAD)0
Skewness24.47679419
Sum63494
Variance246138.0554
MonotonicityNot monotonic
2022-06-07T22:26:32.609372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
01408
96.4%
40011
 
0.8%
5008
 
0.5%
7005
 
0.3%
4504
 
0.3%
6004
 
0.3%
20004
 
0.3%
12002
 
0.1%
4802
 
0.1%
155001
 
0.1%
Other values (11)11
 
0.8%
ValueCountFrequency (%)
01408
96.4%
541
 
0.1%
3501
 
0.1%
40011
 
0.8%
4504
 
0.3%
4802
 
0.1%
5008
 
0.5%
5601
 
0.1%
6004
 
0.3%
6201
 
0.1%
ValueCountFrequency (%)
155001
 
0.1%
83001
 
0.1%
35001
 
0.1%
25001
 
0.1%
20004
0.3%
14001
 
0.1%
13001
 
0.1%
12002
0.1%
11501
 
0.1%
8001
 
0.1%

MoSold
Real number (ℝ≥0)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.321917808
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:32.694280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.703626208
Coefficient of variation (CV)0.4276591836
Kurtosis-0.4041093415
Mean6.321917808
Median Absolute Deviation (MAD)2
Skewness0.2120529851
Sum9230
Variance7.309594675
MonotonicityNot monotonic
2022-06-07T22:26:32.771745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6253
17.3%
7234
16.0%
5204
14.0%
4141
9.7%
8122
8.4%
3106
7.3%
1089
 
6.1%
1179
 
5.4%
963
 
4.3%
1259
 
4.0%
Other values (2)110
7.5%
ValueCountFrequency (%)
158
 
4.0%
252
 
3.6%
3106
7.3%
4141
9.7%
5204
14.0%
6253
17.3%
7234
16.0%
8122
8.4%
963
 
4.3%
1089
 
6.1%
ValueCountFrequency (%)
1259
 
4.0%
1179
 
5.4%
1089
 
6.1%
963
 
4.3%
8122
8.4%
7234
16.0%
6253
17.3%
5204
14.0%
4141
9.7%
3106
7.3%

YrSold
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2009
338 
2007
329 
2006
314 
2008
304 
2010
175 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2007
3rd row2008
4th row2006
5th row2008

Common Values

ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Length

2022-06-07T22:26:32.852369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:32.907114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2009338
23.2%
2007329
22.5%
2006314
21.5%
2008304
20.8%
2010175
12.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SaleType
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
WD
1267 
New
 
122
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.158219178
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD1267
86.8%
New122
 
8.4%
COD43
 
2.9%
ConLD9
 
0.6%
ConLI5
 
0.3%
ConLw5
 
0.3%
CWD4
 
0.3%
Oth3
 
0.2%
Con2
 
0.1%

Length

2022-06-07T22:26:32.982032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:33.044355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
wd1267
86.8%
new122
 
8.4%
cod43
 
2.9%
conld9
 
0.6%
conli5
 
0.3%
conlw5
 
0.3%
cwd4
 
0.3%
oth3
 
0.2%
con2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SaleCondition
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Normal
1198 
Partial
125 
Abnorml
 
101
Family
 
20
Alloca
 
12

Length

Max length7
Median length6
Mean length6.157534247
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal1198
82.1%
Partial125
 
8.6%
Abnorml101
 
6.9%
Family20
 
1.4%
Alloca12
 
0.8%
AdjLand4
 
0.3%

Length

2022-06-07T22:26:33.129400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-06-07T22:26:33.185400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
normal1198
82.1%
partial125
 
8.6%
abnorml101
 
6.9%
family20
 
1.4%
alloca12
 
0.8%
adjland4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SalePrice
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.1959
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2022-06-07T22:26:33.269978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.50288
Coefficient of variation (CV)0.4391000319
Kurtosis6.53628186
Mean180921.1959
Median Absolute Deviation (MAD)38000
Skewness1.88287576
Sum264144946
Variance6311111264
MonotonicityNot monotonic
2022-06-07T22:26:33.374943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000020
 
1.4%
13500017
 
1.2%
15500014
 
1.0%
14500014
 
1.0%
19000013
 
0.9%
11000013
 
0.9%
11500012
 
0.8%
16000012
 
0.8%
13000011
 
0.8%
13900011
 
0.8%
Other values (653)1323
90.6%
ValueCountFrequency (%)
349001
0.1%
353111
0.1%
379001
0.1%
393001
0.1%
400001
0.1%
520001
0.1%
525001
0.1%
550002
0.1%
559931
0.1%
585001
0.1%
ValueCountFrequency (%)
7550001
0.1%
7450001
0.1%
6250001
0.1%
6116571
0.1%
5829331
0.1%
5565811
0.1%
5550001
0.1%
5380001
0.1%
5018371
0.1%
4850001
0.1%

Interactions

2022-06-07T22:26:12.162768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:41.175125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:44.148816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:46.956790image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:49.963568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:53.104348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:56.129385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:59.526349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:02.959562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:06.267561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:09.633363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:12.899361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:16.472401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:19.483361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:22.502832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:25.912954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:29.126458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:32.407207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:35.889007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:39.458348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:42.412984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:45.213656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:48.110308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:51.111067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:54.133770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:57.088173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:00.073815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:03.112472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:06.102241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:09.258057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:12.259338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:41.284123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:44.248815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:47.051699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:50.080573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:53.198394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:56.234346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:59.628396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:03.083561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:06.385564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:09.729371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:13.002365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:16.599363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:19.596364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:22.601830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:26.016958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:29.223049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:32.530203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:36.012006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:39.547348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:42.504599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:45.311652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:48.200812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:51.216071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:54.222772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:57.199210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:00.184800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:03.238468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:06.203241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:09.351016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:12.358337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:41.380966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:44.340816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:47.140702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:50.187565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:53.298346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:56.344346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:59.738346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:03.189565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:06.503846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:09.824368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:13.196408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:16.715362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:19.699408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:22.770831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:26.149954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:29.319049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:32.740204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:36.138008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:39.654602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:42.592599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:45.407531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:48.288473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:51.323063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:54.311770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:57.292211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:00.289808image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:03.339468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:06.396760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:09.444019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:26:12.453779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:41.478927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:44.426934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:47.230577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:50.282615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:53.387393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:56.443345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:24:59.832348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:03.287561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:06.609361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:09.992365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:13.289361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:16.808363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:19.791361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:22.910839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-07T22:25:26.249961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-06-07T22:26:09.154985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-07T22:26:33.917890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-07T22:26:34.216364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-07T22:26:34.582363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-07T22:26:15.509388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-07T22:26:18.762211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-07T22:26:19.710325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-07T22:26:20.048901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
0160RL65.08450PaveNaNRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520032003GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150856GasAExYSBrkr85685401710102131Gd8Typ0NaNAttchd2003.0RFn2548TATAY0610000NaNNaNNaN022008WDNormal208500
1220RL80.09600PaveNaNRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story6819761976GableCompShgMetalSdMetalSdNone0.0TATACBlockGdTAGdALQ978Unf02841262GasAExYSBrkr1262001262012031TA6Typ1TAAttchd1976.0RFn2460TATAY29800000NaNNaNNaN052007WDNormal181500
2360RL68.011250PaveNaNIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story7520012002GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434920GasAExYSBrkr92086601786102131Gd6Typ1TAAttchd2001.0RFn2608TATAY0420000NaNNaNNaN092008WDNormal223500
3470RL60.09550PaveNaNIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story7519151970GableCompShgWd SdngWd ShngNone0.0TATABrkTilTAGdNoALQ216Unf0540756GasAGdYSBrkr96175601717101031Gd7Typ1GdDetchd1998.0Unf3642TATAY035272000NaNNaNNaN022006WDAbnorml140000
4560RL84.014260PaveNaNIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story8520002000GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf04901145GasAExYSBrkr1145105302198102141Gd9Typ1TAAttchd2000.0RFn3836TATAY192840000NaNNaNNaN0122008WDNormal250000
5650RL85.014115PaveNaNIR1LvlAllPubInsideGtlMitchelNormNorm1Fam1.5Fin5519931995GableCompShgVinylSdVinylSdNone0.0TATAWoodGdTANoGLQ732Unf064796GasAExYSBrkr79656601362101111TA5Typ0NaNAttchd1993.0Unf2480TATAY4030032000NaNMnPrvShed700102009WDNormal143000
6720RL75.010084PaveNaNRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520042005GableCompShgVinylSdVinylSdStone186.0GdTAPConcExTAAvGLQ1369Unf03171686GasAExYSBrkr1694001694102031Gd7Typ1GdAttchd2004.0RFn2636TATAY255570000NaNNaNNaN082007WDNormal307000
7860RLNaN10382PaveNaNIR1LvlAllPubCornerGtlNWAmesPosNNorm1Fam2Story7619731973GableCompShgHdBoardHdBoardStone240.0TATACBlockGdTAMnALQ859BLQ322161107GasAExYSBrkr110798302090102131TA7Typ2TAAttchd1973.0RFn2484TATAY235204228000NaNNaNShed350112009WDNormal200000
8950RM51.06120PaveNaNRegLvlAllPubInsideGtlOldTownArteryNorm1Fam1.5Fin7519311950GableCompShgBrkFaceWd ShngNone0.0TATABrkTilTATANoUnf0Unf0952952GasAGdYFuseF102275201774002022TA8Min12TADetchd1931.0Unf2468FaTAY900205000NaNNaNNaN042008WDAbnorml129900
910190RL50.07420PaveNaNRegLvlAllPubCornerGtlBrkSideArteryArtery2fmCon1.5Unf5619391950GableCompShgMetalSdMetalSdNone0.0TATABrkTilTATANoGLQ851Unf0140991GasAExYSBrkr1077001077101022TA5Typ2TAAttchd1939.0RFn1205GdTAY040000NaNNaNNaN012008WDNormal118000

Last rows

IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondYearBuiltYearRemodAddRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesFireplaceQuGarageTypeGarageYrBltGarageFinishGarageCarsGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePrice
1450145190RL60.09000PaveNaNRegLvlAllPubFR2GtlNAmesNormNormDuplex2Story5519741974GableCompShgVinylSdVinylSdNone0.0TATACBlockGdTANoUnf0Unf0896896GasATAYSBrkr89689601792002242TA8Typ0NaNNaNNaNNaN00NaNNaNY32450000NaNNaNNaN092009WDNormal136000
1451145220RL78.09262PaveNaNRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story8520082009GableCompShgCemntBdCmentBdStone194.0GdTAPConcGdTANoUnf0Unf015731573GasAExYSBrkr1578001578002031Ex7Typ1GdAttchd2008.0Fin3840TATAY0360000NaNNaNNaN052009NewPartial287090
14521453180RM35.03675PaveNaNRegLvlAllPubInsideGtlEdwardsNormNormTwnhsESLvl5520052005GableCompShgVinylSdVinylSdBrkFace80.0TATAPConcGdTAGdGLQ547Unf00547GasAGdYSBrkr1072001072101021TA5Typ0NaNBasment2005.0Fin2525TATAY0280000NaNNaNNaN052006WDNormal145000
1453145420RL90.017217PaveNaNRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story5520062006GableCompShgVinylSdVinylSdNone0.0TATAPConcGdTANoUnf0Unf011401140GasAExYSBrkr1140001140001031TA6Typ0NaNNaNNaNNaN00NaNNaNY36560000NaNNaNNaN072006WDAbnorml84500
1454145520FV62.07500PavePaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story7520042005GableCompShgVinylSdVinylSdNone0.0GdTAPConcGdTANoGLQ410Unf08111221GasAExYSBrkr1221001221102021Gd6Typ0NaNAttchd2004.0RFn2400TATAY01130000NaNNaNNaN0102009WDNormal185000
1455145660RL62.07917PaveNaNRegLvlAllPubInsideGtlGilbertNormNorm1Fam2Story6519992000GableCompShgVinylSdVinylSdNone0.0TATAPConcGdTANoUnf0Unf0953953GasAExYSBrkr95369401647002131TA7Typ1TAAttchd1999.0RFn2460TATAY0400000NaNNaNNaN082007WDNormal175000
1456145720RL85.013175PaveNaNRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story6619781988GableCompShgPlywoodPlywoodStone119.0TATACBlockGdTANoALQ790Rec1635891542GasATAYSBrkr2073002073102031TA7Min12TAAttchd1978.0Unf2500TATAY34900000NaNMnPrvNaN022010WDNormal210000
1457145870RL66.09042PaveNaNRegLvlAllPubInsideGtlCrawforNormNorm1Fam2Story7919412006GableCompShgCemntBdCmentBdNone0.0ExGdStoneTAGdNoGLQ275Unf08771152GasAExYSBrkr1188115202340002041Gd9Typ2GdAttchd1941.0RFn1252TATAY0600000NaNGdPrvShed250052010WDNormal266500
1458145920RL68.09717PaveNaNRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story5619501996HipCompShgMetalSdMetalSdNone0.0TATACBlockTATAMnGLQ49Rec102901078GasAGdYFuseA1078001078101021Gd5Typ0NaNAttchd1950.0Unf1240TATAY3660112000NaNNaNNaN042010WDNormal142125
1459146020RL75.09937PaveNaNRegLvlAllPubInsideGtlEdwardsNormNorm1Fam1Story5619651965GableCompShgHdBoardHdBoardNone0.0GdTACBlockTATANoBLQ830LwQ2901361256GasAGdYSBrkr1256001256101131TA6Typ0NaNAttchd1965.0Fin1276TATAY736680000NaNNaNNaN062008WDNormal147500